OpenRaiser/Intern-Atlas huggingface dataset:
AI Safety in the Eyes of the Downstream Developer: A First Look at Concerns, Practices, and Challenges
Venue: arXiv cs.SE 2025
Year: 2025

Pre-trained models (PTMs) have become a cornerstone of AI-based software, allowing for rapid integration and development with minimal training overhead. However, their adoption also introduces unique safety challenges, such as data leakage and biased outputs, that demand rigorous handling by downstream developers. While previous research has proposed taxonomies of AI safety concerns and various mitigation strategies, how downstream developers address these issues remains unexplored. This study investigates downstream developers' concerns, practices and perceived challenges regarding AI safety issues during AI-based software development. To achieve this, we conducted a mixed-method study, including interviews with 18 participants, a survey of 86 practitioners, and an analysis of 874 AI incidents from the AI Incident Database. Our results reveal that while developers generally demonstrate strong awareness of AI safety concerns, their practices, especially during the preparation and PTM selection phases, are often inadequate. The lack of concrete guidelines and policies leads to significant variability in the comprehensiveness of their safety approaches throughout the development lifecycle, with additional challenges such as poor documentation and knowledge gaps, further impeding effective implementation. Based on our findings, we offer suggestions for PTM developers, AI-based software developers, researchers, and policy makers to enhance the integration of AI safety measures.
KSHSeek: Data-Driven Approaches to Mitigating and Detecting Knowledge-Shortcut Hallucinations in Generative Models
Venue: arXiv cs.CL 2025
Year: 2025

The emergence of large language models (LLMs) has significantly advanced the development of natural language processing (NLP), especially in text generation tasks like question answering. However, model hallucinations remain a major challenge in natural language generation (NLG) tasks due to their complex causes. We systematically expand on the causes of factual hallucinations from the perspective of knowledge shortcuts, analyzing hallucinations arising from correct and defect-free data and demonstrating that knowledge-shortcut hallucinations are prevalent in generative models. To mitigate this issue, we propose a high similarity pruning algorithm at the data preprocessing level to reduce spurious correlations in the data. Additionally, we design a specific detection method for knowledge-shortcut hallucinations to evaluate the effectiveness of our mitigation strategy. Experimental results show that our approach effectively reduces knowledge-shortcut hallucinations, particularly in fine-tuning tasks, without negatively impacting model performance in question answering. This work introduces a new paradigm for mitigating specific hallucination issues in generative models, enhancing their robustness and reliability in real-world applications.
Pose-Based Fall Detection System: Efficient Monitoring on Standard CPUs
Venue: arXiv cs.CV 2025
Year: 2025

Falls among elderly residents in assisted living homes pose significant health risks, often leading to injuries and a decreased quality of life. Current fall detection solutions typically rely on sensor-based systems that require dedicated hardware, or on video-based models that demand high computational resources and GPUs for real-time processing. In contrast, this paper presents a robust fall detection system that does not require any additional sensors or high-powered hardware. The system uses pose estimation techniques, combined with threshold-based analysis and a voting mechanism, to effectively distinguish between fall and non-fall activities. For pose detection, we leverage MediaPipe, a lightweight and efficient framework that enables real-time processing on standard CPUs with minimal computational overhead. By analyzing motion, body position, and key pose points, the system processes pose features with a 20-frame buffer, minimizing false positives and maintaining high accuracy even in real-world settings. This unobtrusive, resource-efficient approach provides a practical solution for enhancing resident safety in old age homes, without the need for expensive sensors or high-end computational resources.
Cryptoscope: Analyzing cryptographic usages in modern software
Venue: arXiv cs.CR 2025
Year: 2025

The advent of quantum computing poses a significant challenge as it has the potential to break certain cryptographic algorithms, necessitating a proactive approach to identify and modernize cryptographic code. Identifying these cryptographic elements in existing code is only the first step. It is crucial not only to identify quantum vulnerable algorithms but also to detect vulnerabilities and incorrect crypto usages, to prioritize, report, monitor as well as remediate and modernize code bases. A U.S. government memorandum require agencies to begin their transition to PQC (Post Quantum Cryptograpy) by conducting a prioritized inventory of cryptographic systems including software and hardware systems. In this paper we describe our code scanning tool - Cryptoscope - which leverages cryptographic domain knowledge as well as compiler techniques to statically parse and analyze source code. By analyzing control and data flow the tool is able to build an extendable and querriable inventory of cryptography. Cryptoscope goes beyond identifying disconnected cryptographic APIs and instead provides the user with an inventory of cryptographic assets - containing comprehensive views of the cryptographic operations implemented. We show that for more than 92% of our test cases, these views include the cryptographic operation itself, APIs, as well as the related material such as keys, nonces, random sources etc. Lastly, building on top of this inventory, our tool is able to detect and report all the cryptographic related weaknesses and vulnerabilities (11 out of 15) in CamBench - achieving state-of-the-art performance.
Empirical Analysis of the Impact of 5G Jitter on Time-Aware Shaper Scheduling in a 5G-TSN Network
Venue: arXiv cs.NI 2025
Year: 2025

Deterministic communications are essential for industrial automation, ensuring strict latency requirements and minimal jitter in packet transmission. Modern production lines, specializing in robotics, require higher flexibility and mobility, which drives the integration of Time-Sensitive Networking (TSN) and 5G networks in Industry 4.0. TSN achieves deterministic communications by using mechanisms such as the IEEE 802.1Qbv Time-Aware Shaper (TAS), which schedules packet transmissions within precise cycles, thereby reducing latency, jitter, and congestion. 5G networks complement TSN by providing wireless mobility and supporting ultra-Reliable Low-Latency Communications. However, 5G channel effects such as fast fading, interference, and network-induced latency and jitter can disrupt TSN traffic, potentially compromising deterministic scheduling and performance. This paper presents an empirical analysis of 5G network latency and jitter on IEEE 802.1Qbv performance in a 5G-TSN network. We evaluate the impact of 5G integration on TSN's deterministic scheduling through a testbed combining IEEE 802.1Qbv-enabled switches, TSN translators, and a commercial 5G system. Our results show that, with proper TAS configuration in the TSN switch aligned with the 5G system, jitter can be mitigated, maintaining deterministic performance.
Post-Hoc Calibrated Anomaly Detection
Venue: arXiv cs.LG 2025
Year: 2025

Deep unsupervised anomaly detection has seen improvements in a supervised binary classification paradigm in which auxiliary external data is included in the training set as anomalous data in a process referred to as outlier exposure, which opens the possibility of exploring the efficacy of post-hoc calibration for anomaly detection and localization. Post-hoc Platt scaling and Beta calibration are found to improve results with gradient-based input perturbation, as well as post-hoc training with a strictly proper loss of a base model initially trained on an unsupervised loss. Post-hoc calibration is also found at times to be more effective using random synthesized spectral data as labeled anomalous data in the calibration set, suggesting that outlier exposure is superior only for initial training.
Red Teaming with Artificial Intelligence-Driven Cyberattacks: A Scoping Review
Venue: arXiv cs.CR 2025
Year: 2025

The progress of artificial intelligence (AI) has made sophisticated methods available for cyberattacks and red team activities. These AI attacks can automate the process of penetrating a target or collecting sensitive data. The new methods can also accelerate the execution of the attacks. This review article examines the use of AI technologies in cybersecurity attacks. It also tries to describe typical targets for such attacks. We employed a scoping review methodology to analyze articles and identify AI methods, targets, and models that red teams can utilize to simulate cybercrime. From the 470 records screened, 11 were included in the review. Various cyberattack methods were identified, targeting sensitive data, systems, social media profiles, passwords, and URLs. The application of AI in cybercrime to develop versatile attack models presents an increasing threat. Furthermore, AI-based techniques in red team use can provide new ways to address these issues.
Risk-Aware Reinforcement Learning for Autonomous Driving: Improving Safety When Driving through Intersection
Venue: arXiv cs.RO 2025
Year: 2025

Applying reinforcement learning to autonomous driving has garnered widespread attention. However, classical reinforcement learning methods optimize policies by maximizing expected rewards but lack sufficient safety considerations, often putting agents in hazardous situations. This paper proposes a risk-aware reinforcement learning approach for autonomous driving to improve the safety performance when crossing the intersection. Safe critics are constructed to evaluate driving risk and work in conjunction with the reward critic to update the actor. Based on this, a Lagrangian relaxation method and cyclic gradient iteration are combined to project actions into a feasible safe region. Furthermore, a Multi-hop and Multi-layer perception (MLP) mixed Attention Mechanism (MMAM) is incorporated into the actor-critic network, enabling the policy to adapt to dynamic traffic and overcome permutation sensitivity challenges. This allows the policy to focus more effectively on surrounding potential risks while enhancing the identification of passing opportunities. Simulation tests are conducted on different tasks at unsignalized intersections. The results show that the proposed approach effectively reduces collision rates and improves crossing efficiency in comparison to baseline algorithms. Additionally, our ablation experiments demonstrate the benefits of incorporating risk-awareness and MMAM into RL.
Reverse-Engineering the Retrieval Process in GenIR Models
Venue: arXiv cs.IR 2025
Year: 2025

Generative Information Retrieval (GenIR) is a novel paradigm in which a transformer encoder-decoder model predicts document rankings based on a query in an end-to-end fashion. These GenIR models have received significant attention due to their simple retrieval architecture while maintaining high retrieval effectiveness. However, in contrast to established retrieval architectures like cross-encoders or bi-encoders, their internal computations remain largely unknown. Therefore, this work studies the internal retrieval process of GenIR models by applying methods based on mechanistic interpretability, such as patching and vocabulary projections. By replacing the GenIR encoder with one trained on fewer documents, we demonstrate that the decoder is the primary component responsible for successful retrieval. Our patching experiments reveal that not all components in the decoder are crucial for the retrieval process. More specifically, we find that a pass through the decoder can be divided into three stages: (I) the priming stage, which contributes important information for activating subsequent components in later layers; (II) the bridging stage, where cross-attention is primarily active to transfer query information from the encoder to the decoder; and (III) the interaction stage, where predominantly MLPs are active to predict the document identifier. Our findings indicate that interaction between query and document information occurs only in the last stage. We hope our results promote a better understanding of GenIR models and foster future research to overcome the current challenges associated with these models.
Inducing Personality in LLM-Based Honeypot Agents: Measuring the Effect on Human-Like Agenda Generation
Venue: arXiv cs.AI 2025
Year: 2025

This paper presents SANDMAN, an architecture for cyber deception that leverages Language Agents to emulate convincing human simulacra. Our 'Deceptive Agents' serve as advanced cyber decoys, designed for high-fidelity engagement with attackers by extending the observation period of attack behaviours. Through experimentation, measurement, and analysis, we demonstrate how a prompt schema based on the five-factor model of personality systematically induces distinct 'personalities' in Large Language Models. Our results highlight the feasibility of persona-driven Language Agents for generating diverse, realistic behaviours, ultimately improving cyber deception strategies.
The impact of digital media-driven affective polarisation on epidemic dynamics
Venue: arXiv cs.SI 2025
Year: 2025

While prior studies have examined the influence of information diffusion on epidemic dynamics, the role of affective polarisation--driven by digital media usage--remains less understood. This study introduces a mathematical framework to quantify the interplay between affective polarisation and epidemic spread, revealing contrasting effects depending on transmission rates. The model demonstrates that greater digital media influence leads to increased polarisation. Notably, the results reveal opposing trends: a negative correlation between polarisation and the infected population is observed when transmission rates are low, whereas a positive correlation emerges in high-transmission scenarios. These findings provide a quantitative foundation for assessing how digital media-driven polarisation may exacerbate health crises, informing future public health strategies.
Brain Organoid Computing -- an Overview
Venue: arXiv cs.ET 2025
Year: 2025

The aim of this paper is to give an overview of brain organoid computing, its characteristics, challenges, as well as possible advantages for future applications in the field of artificial intelligence. An important part is the extensive bibliography covering all relevant aspects and questions on this topic. Brain organoids - three-dimensional in vitro neural structures derived from human stem cells - have recently garnered attention not only in medical research but also as potential substrates for unconventional computing. Their biological nature allows them to exhibit learning behavior, plasticity, and parallel information processing, making them fundamentally different from traditional silicon-based systems. This opens up new perspectives on how intelligent systems might be designed in the future. Using brain organoids for computing presents a possible pathway towards more adaptive, energy-efficient, and biologically inspired forms of AI. However, challenges persist, particularly regarding lifespan, interfacing, reproducibility, and ethical concerns regarding the use of human-derived tissue. This paper aims to provide a foundational understanding for researchers exploring the convergence of human biology and computation.
Gemma 3 Technical Report
Venue: arXiv cs.CL 2025
Year: 2025

We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision understanding abilities, a wider coverage of languages and longer context - at least 128K tokens. We also change the architecture of the model to reduce the KV-cache memory that tends to explode with long context. This is achieved by increasing the ratio of local to global attention layers, and keeping the span on local attention short. The Gemma 3 models are trained with distillation and achieve superior performance to Gemma 2 for both pre-trained and instruction finetuned versions. In particular, our novel post-training recipe significantly improves the math, chat, instruction-following and multilingual abilities, making Gemma3-4B-IT competitive with Gemma2-27B-IT and Gemma3-27B-IT comparable to Gemini-1.5-Pro across benchmarks. We release all our models to the community.
SeLIP: Similarity Enhanced Contrastive Language Image Pretraining for Multi-modal Head MRI
Venue: arXiv cs.CV 2025
Year: 2025

Despite that deep learning (DL) methods have presented tremendous potential in many medical image analysis tasks, the practical applications of medical DL models are limited due to the lack of enough data samples with manual annotations. By noting that the clinical radiology examinations are associated with radiology reports that describe the images, we propose to develop a foundation model for multi-model head MRI by using contrastive learning on the images and the corresponding radiology findings. In particular, a contrastive learning framework is proposed, where a mixed syntax and semantic similarity matching metric is integrated to reduce the thirst of extreme large dataset in conventional contrastive learning framework. Our proposed similarity enhanced contrastive language image pretraining (SeLIP) is able to effectively extract more useful features. Experiments revealed that our proposed SeLIP performs well in many downstream tasks including image-text retrieval task, classification task, and image segmentation, which highlights the importance of considering the similarities among texts describing different images in developing medical image foundation models.
LENVIZ: A High-Resolution Low-Exposure Night Vision Benchmark Dataset
Venue: arXiv cs.CV 2025
Year: 2025

Low-light image enhancement is crucial for a myriad of applications, from night vision and surveillance, to autonomous driving. However, due to the inherent limitations that come in hand with capturing images in low-illumination environments, the task of enhancing such scenes still presents a formidable challenge. To advance research in this field, we introduce our Low Exposure Night Vision (LENVIZ) Dataset, a comprehensive multi-exposure benchmark dataset for low-light image enhancement comprising of over 230K frames showcasing 24K real-world indoor and outdoor, with-and without human, scenes. Captured using 3 different camera sensors, LENVIZ offers a wide range of lighting conditions, noise levels, and scene complexities, making it the largest publicly available up-to 4K resolution benchmark in the field. LENVIZ includes high quality human-generated ground truth, for which each multi-exposure low-light scene has been meticulously curated and edited by expert photographers to ensure optimal image quality. Furthermore, we also conduct a comprehensive analysis of current state-of-the-art low-light image enhancement techniques on our dataset and highlight potential areas of improvement.
Domain-incremental White Blood Cell Classification with Privacy-aware Continual Learning
Venue: arXiv cs.LG 2025
Year: 2025

White blood cell (WBC) classification plays a vital role in hematology for diagnosing various medical conditions. However, it faces significant challenges due to domain shifts caused by variations in sample sources (e.g., blood or bone marrow) and differing imaging conditions across hospitals. Traditional deep learning models often suffer from catastrophic forgetting in such dynamic environments, while foundation models, though generally robust, experience performance degradation when the distribution of inference data differs from that of the training data. To address these challenges, we propose a generative replay-based Continual Learning (CL) strategy designed to prevent forgetting in foundation models for WBC classification. Our method employs lightweight generators to mimic past data with a synthetic latent representation to enable privacy-preserving replay. To showcase the effectiveness, we carry out extensive experiments with a total of four datasets with different task ordering and four backbone models including ResNet50, RetCCL, CTransPath, and UNI. Experimental results demonstrate that conventional fine-tuning methods degrade performance on previously learned tasks and struggle with domain shifts. In contrast, our continual learning strategy effectively mitigates catastrophic forgetting, preserving model performance across varying domains. This work presents a practical solution for maintaining reliable WBC classification in real-world clinical settings, where data distributions frequently evolve.
Contextual Metric Meta-Evaluation by Measuring Local Metric Accuracy
Venue: arXiv cs.CL 2025
Year: 2025

Meta-evaluation of automatic evaluation metrics -- assessing evaluation metrics themselves -- is crucial for accurately benchmarking natural language processing systems and has implications for scientific inquiry, production model development, and policy enforcement. While existing approaches to metric meta-evaluation focus on general statements about the absolute and relative quality of metrics across arbitrary system outputs, in practice, metrics are applied in highly contextual settings, often measuring the performance for a highly constrained set of system outputs. For example, we may only be interested in evaluating a specific model or class of models. We introduce a method for contextual metric meta-evaluation by comparing the local metric accuracy of evaluation metrics. Across translation, speech recognition, and ranking tasks, we demonstrate that the local metric accuracies vary both in absolute value and relative effectiveness as we shift across evaluation contexts. This observed variation highlights the importance of adopting context-specific metric evaluations over global ones.
Guarding against artificial intelligence--hallucinated citations: the case for full-text reference deposit
Venue: arXiv cs.DL 2025
Year: 2025

The tendency of generative artificial intelligence (AI) systems to "hallucinate" false information is well-known; AI-generated citations to non-existent sources have made their way into the reference lists of peer-reviewed publications. Here, I propose a solution to this problem, taking inspiration from the Transparency and Openness Promotion (TOP) data sharing guidelines, the clash of generative AI with the American judiciary, and the precedent set by submissions of prior art to the United States Patent and Trademark Office. Journals should require authors to submit the full text of each cited source along with their manuscripts, thereby preventing authors from citing any material whose full text they cannot produce. This solution requires limited additional work on the part of authors or editors while effectively immunizing journals against hallucinated references.